Challenges and Opportunities of Big Data in the Modern World

This report delves into the multifaceted world of big data, examining its challenges and opportunities. It begins by defining big data and its significance in today's data-driven world, emphasizing its role in revealing trends and patterns. The report highlights the 5Vs of big data: Volume, Velocity, Variety, Veracity, and Value, explaining each dimension and the challenges they pose. It further explores the application of big data in various sectors, including social media, and discusses how businesses can leverage data analytics to gain valuable insights. The report also addresses the benefits of big data, such as improved decision-making and innovation, while also acknowledging the challenges associated with its management and analysis. Overall, the report provides a comprehensive overview of big data, its implications, and its potential for transforming businesses and industries.

Running head: CHALLENGES AND OPPORTUNITIES OF BIG DATA 1
CHALLENGES AND OPPORTUNITIES OF BIG DATA
Name of Student
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CHALLENGES AND OPPORTUNITIES OF BIG DATA 2
Introduction
Big data may be described as extremely large sets of data that can be analyzed in order to
disclose trends, patterns as well as associations that are mostly related to human being interaction
and behavior. The analysis is often carried out computationally. The data is often considered too
complex such that it cannot be handled effectively using traditional application software for data
processing. Big data analytics is therefore used to examine large sets of data in a bid to discover
these trends, patterns, correlations as well as other useful insights. The world has kept on
witnessing several emerging technologies which makes it possible to carry out analysis of data as
well as immediately find answers from it (Cardenas, Manadhata, & Rajan, 2013). Big data
analytics is among such technologies that are designed to require less effort and are equally more
efficient than traditional business intelligence solutions. The concept of big data has been in
existence for several years and has consistently been used by several business organizations in
order to seize all the data that comes into their businesses. Once the data has been captured, the
organizations can them apply data analytics to obtain value that is significant to their operations.
About 2.5 quintillion bytes are data can be created daily. The data originates from videos,
pictures, numerous posts made on social media, intelligent sensors, cell phone GPS signals,
records on purchases transactions, among others. All these data make up big data. There also
exists a possibility that big data as well as what it is done with has got the ability to be a major
driving force that leads to innovation and creation of value. The concept of big data and big data
analytics brings a lot of benefits to any business organization that adopts its use. In the current
century, there has been witnessed an increased adoption in the use of big data analytics in several
business organizations (Chen, Chiang, & Storey, 2012). The increased usage has also seen a
number of valuable opportunities also emerge. Such opportunities can be put to practice in order

CHALLENGES AND OPPORTUNITIES OF BIG DATA 3
to increase the gain from big data. Besides the number of benefits that come along with the
adoption of big data analytics also comes with a number of challenges. These challenges often
come several adverse effects that any business organizations has to put in place measures to
counter the effects. Big data can be looked into in different perspectives, that is, the social good
perspective, the technological perspective and the business perspective.
Need for big data
Big data for every business organization or industry always functions as a major factor that
promotes production. It is estimated that 7 Exabyte of new data enterprises that exist globally
were stored within just a period of one year (Cuzzocrea, Song & Davis, 2011). Another
interesting factor is that more than 50% of IP traffic is non-human and that there will be an
increased importance of M2M. This makes it essential to comprehend what really big data is
intended to achieve. The following questions will help understand the need for understanding big
data.
 What is big data supposed to create?
 What exact value should big data create?
The increase in complexity of big data has become a major challenge that all organization
managements should aim at addressing. All business organizations globally must be able to store
the data and also leverage it more effectively and fast in order to achieve value for the business
(Fisher et al, 2012). Value will originate from whatever is inferred from it and this can be
achieved through data analytics. The process of deriving value can be achieved by analyzing big
data and the achievements can be as follows;
 Developing transparency

CHALLENGES AND OPPORTUNITIES OF BIG DATA 4
 Segmenting customers
 Supporting/ replacing decision making for humans with algorithms that are automated.
 Discovering vital needs, improving performance and exposing variability.
 Innovating new products, services and business models.
Big data is today used in several sectors and can be used to derive financial values from those
sectors. Some of the common sectors that make use of big data include;
 Professional data/ social personal (e.g. you tube, twitter, Instagram, Facebook etc.)
 Global personal location data- most commonly used due to the rise of mobile devices.
 Health care- this is relevant because patients’ records and general health information are
always considered critical.
 Manufacturing
 Public sector administration- also relevant due to the proposal that aims at creating
business sector information
 Retail- most large web retail shops need big data for their activities.
Big data in social media
For over a decade, big data and social media has become so interconnected to such an extent that
they are almost being synonymous in some circles. Recent studies reveal that a major part of all
the data that is generated is related to or does originate from social media. Most of these data
comes in unstructured forms which therefore makes it necessary for data analytics in order to at
least make sense and turn all this information into some value (Gandomi & Haider, 2015).
Marketers and business analysts that have knowledge on the concept of data analytics have
gained valuable knowledge on the behavior of their target audiences as well as what to expect

CHALLENGES AND OPPORTUNITIES OF BIG DATA 5
from them. This has also improved the concept of social media marketing. The idea behind
modern social media analytics is to determine the portion of data, from the large data available
online, that can have noteworthy value and also how to make sense from the data obtained
(Herodotou et al, 2011).
A study carried out to determine the world internet activity, it was realized that for every one
minute, there are;
 701,389 Facebook logins
 150 sent emails
 527,760 photos shared on snapchat
 20.8 million WhatsApp texts
 2.78 million YouTube views
 28,194 new Instagram posts
 2.4 million google search queries
 1.04 million vine loops
The statistics keep growing symbolizing a continuous rise in the number of social media
activities. The primary aim of every marketer is therefore how they can utilize such huge amount
of data that is gathered at least every second (Hu et al, 2014). Many business organizations in
many cases have individuals who monitor their social accounts and a lot of effort is put to ensure
the information is analyzed and enough value gained from the data collected.
Challenges of big data
Despite several promises and benefits from the usage of big data, there equally exists several big
data problems that have different characteristics and therefore make the idea technically

CHALLENGES AND OPPORTUNITIES OF BIG DATA 6
challenging. The challenges can be grouped into different categories namely; management,
process and data (Kambatla et al, 2014). In a bid to assist in the analysis of big data, there exists
the 5Vs OF BIG DATA that will help in making such analysis. The 5Vs of big data that will be
discussed are; velocity, volume, veracity, variety and value.
5Vs of big data
Volume
This represents the volume of data that is exploding and how fast the data keeps growing each
year given the new data sources that keep emerging over time. It is the magnitude that is being
collected and generated. The volume of data that is generated keeps growing at a faster rate.
Studies also reveal that social media plays a key role for the increasing magnitude of data being
generated. This is also enhanced by the common use of mobile devices. Twitter and Facebook
generate up to 7 TB and 10 TB of data on a daily basis. Classifying big data on the basis of
volume is relative with regard to time and the data type that is generated. Different data types
require different technologies for data management. Data also comes in different sizes and for
specific size, it requires different data storage technique (LaValle et al, 2011).
The major challenge is therefore how properly handle the different sizes of data.
Velocity
Velocity is used to refer to the generation rate of data. Most commonly used data analytics are
based on the making updates that are periodic, most commonly daily, weekly or even monthly.
Due to the rising rate of data generation, there is need to process and analyze big data in real time
so as to be able to make decisions. Time therefore plays a very critical role here. Data that is
generated from mobile applications such as transaction history, demographics and geographical

CHALLENGES AND OPPORTUNITIES OF BIG DATA 7
locations can be used to provide real time personalized services to customers (Maltby, 2011).
This feature is important for any business set up since it helps improve the service levels for
customers and also retain the customers.
The key challenge is therefore how to handle the flood of information in the time required by the
application.
Variety
Variety is all about the different data types that are generated. It involves combining multiple
data sets. Such types of data extend past structured data and can be grouped under unstructured
and semi structured data categories. Up to 80% of the information available today is
unstructured and characteristically too big to handle effectively. Each and every organization
always tries to combine all this data and then analyze it together in the most current ways. Data
can be generated from smart devices, sensors as well as social collaboration technologies
(Moniruzzaman, & Hossain, 2013). Such data is not only structured, but raw, unstructured, semi
structured data from web log files, web pages, e-mails, sensor data, search indexes, documents
etc.
The main challenge for variety is how to handle the multiple types, formats and sources of big
data.
Veracity
Veracity is used to represent the unreliability that is related to the sources of the data. This makes
it important to distinguish the difference between reliable data and uncertain data and then find
how to handle the uncertainty that is associated with the data. It is difficult to distinguish whether
the data analyzed is perfect or complete (Najafabadi et al, 2015). A good process has the

CHALLENGES AND OPPORTUNITIES OF BIG DATA 8
possibility of making bad decisions based on bad data. Veracity comes with a number of
challenges.
 The quality of the data and how broad is its coverage.
 The availability of data
 How well the sampling biases are understood
 How fine the sampling resolution is and how frequently the readings can be taken.
 How well we are able to cope with imprecision, uncertainty, missing statements, missing
values and untruths.
Value
The main idea of value is to ensure that the big data can be transformed into something worthy or
otherwise it will be considered useless. The value of big data must major on its cost as well as
the benefits of gathering and manipulating data. Big data should be viewed as a money pit. The
main challenge here is to ensure that all the available big data is turned into good value by the
organization (Rajaraman, 2016).
Opportunities of big data
Big data, when carefully formatted and correlated in such a way that it makes a lot of sense to
data analysts, has got the potential of developing a number of opportunities for business
organizations. A lot of information can be gleaned from information that is gathered from
popular sites such as twitter, Facebook, google, Instagram etc. by small businesses or advertising
agencies (Russom, 2011). For instance, when using google trends, one is able to ride on the
Google’s information coattails. Marketers can also collect information from third party sources

CHALLENGES AND OPPORTUNITIES OF BIG DATA 9
of data collection when relying on platforms that do not offer a lot of data to their users. One
example of such platform is Instagram.
A major opportunity that has been commonly used is the Facebook ads strategy. Being one of the
biggest suppliers of big data, they have equally ensured that they can get value from such a
magnitude of information (Srinivasa, & Bhatnagar, 2012). Facebook has been collecting millions
of information over the past decade. Since the information cannot be sold directly, they can
instead sell the results of their research to third parties in an anonymous state. Facebook also had
its own advertising platform that enables marketers to fully exploit the availability big data and
therefore maximize the effectiveness of their campaign.
Conclusion
We currently live in an era where big data is largely involved in quite a number of day to day
activities. By using better data analysis for the large chunks of data that are consistently
becoming available, it brings a potential and a possibility of making more advances to several
scientific disciplines (Zakir, Seymour, & Berg, 2015). This whole process will help increase the
profitability and therefore the ability to succeed for many business organizations. There exists
several opportunities that can still be utilized in order to achieve more benefits from big data.
Some of the possible opportunities have been discussed. There however also exists a number of
challenges related to technicality that may hinder identification of big data potential. It is
important for any business organization to properly address those challenges. Such challenges
have been discussed above and measures that can be performed in order to avert the challenges.
A recommendation is also put forward to encourage and support research towards tackling the
identified technical challenges (Zikopoulos, & Eaton, 2011).

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